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  1. Free, publicly-accessible full text available June 1, 2023
  2. Free, publicly-accessible full text available March 1, 2023
  3. In this paper, a novel data offloading decision-making framework is proposed, where users have the option to partially offload their data to a complex Multi-access Edge Computing (MEC) environment, consisting of both ground and UAV-mounted MEC servers. The problem is treated under the perspective of risk-aware user behavior as captured via prospect-theoretic utility functions, while accounting for the inherent computing environment uncertainties. The UAV-mounted MEC servers act as a common pool of resources with potentially superior but uncertain payoff for the users, while the local computation and ground server alternatives constitute safe and guaranteed options, respectively. The optimal user task offloading to the available computing choices is formulated as a maximization problem of each user's satisfaction, and confronted as a non-cooperative game. The existence and uniqueness of a Pure Nash Equilibrium (PNE) are proven, and convergence to the PNE is shown. Detailed numerical results highlight the convergence of the system to the PNE in few only iterations, while the impact of user behavior heterogeneity is evaluated. The introduced framework's consideration of the user risk-aware characteristics and computing uncertainties, results to a sophisticated exploitation of the system resources, which in turn leads to superior users' experienced performance compared to alternative approaches.
  4. Recent technological advances in the use of Unmanned Aerial Vehicles (UAVs) and Wireless Powered Communications (WPC) have enabled the energy efficient operation of the Public Safety Networks (PSN) during disaster scenarios. In this paper, an energy efficient information flow and energy harvesting framework capturing users' risk-aware characteristics is introduced based on the principles of Contract Theory. To better support the operational effectiveness of the proposed framework, users are clustered in rescue groups following a socio-physical-aware group formation mechanism, while rescue leaders for each group are selected. A reinforcement learning approach is applied to enable the optimal matching between the UAVs and the rescue leaders in a distributed and efficient manner. The proposed contract-theoretic framework models the UAVs-victims relation based on a labor market setting via offering rewards to the users (incentives) in order to compensate them for their invested labor (reporting information). Detailed numerical results demonstrate the benefits and superiority of the proposed framework under different settings.